CoChat
Sign Up
Cited by 5Protein Structure and DynamicsParkinson's Disease Mechanisms and TreatmentsEnzyme Structure and Function

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Visualizing gaussian-chain like structural models of human α-synuclein in monomeric pre-fibrillar state: Solution SAXS data and modeling analysis

Open access
Dey, Madhumita, Gupta, Arpit, Badmalia, Maulik D., Ashish, Sharma, Deepak·2025·International Journal of Biological Macromolecules
DOI 10.1016/j.ijbiomac.2024.138614

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

Here, using small angle X-ray scattering (SAXS) data profile as reference, we attempted to visualize conformational ensemble accessible prefibrillar monomeric state of α-synuclein in solution. In agreement with previous reports, our analysis also confirmed that α-synuclein molecules adopted disordered shape profile under non-associating conditions. Chain-ensemble modeling protocol with dummy residues provided two weighted averaged clusters of semi-extended shapes. Further, Ensemble Optimization Method (EOM) computed mole fractions of semi-extended "twisted" conformations which might co-exist in solution. Since these were only C α traces of the models, ALPHAFOLD2 server was used to search for all-atom models. Comparison with experimental data showed all predicted models disagreed equally, as individuals. Finally, we employed molecular dynamics simulations and normal mode analysis-based search coupled with SAXS data to seek better agreeing models. Overall, our analysis concludes that a shifting equilibrium of curved models with low α-helical content best-represents non-associating monomeric α-synuclein.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.ijbiomac.2024.138614

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 94

  1. Mhyre, 2012, Parkinson's disease, Sub-cellular biochemistry, 65, 389, 10.1007/978-94-007-5416-4_16 · DOI
  2. Maroteaux, 1988, Synuclein: a neuron-specific protein localized to the nucleus and presynaptic nerve terminal, Journal of Neuroscience, 8, 2804, 10.1523/JNEUROSCI.08-08-02804.1988 · DOI
  3. Vamvaca, 2009, The first N-terminal amino acids of α-synuclein are essential for α-helical structure formation in vitro and membrane binding in yeast, Journal of molecular biology, 389, 413, 10.1016/j.jmb.2009.03.021 · DOI
  4. Davidson, 1998, Stabilization of α-synuclein secondary structure upon binding to synthetic membranes, Journal of Biological Chemistry, 273, 9443, 10.1074/jbc.273.16.9443 · DOI
  5. Tsigelny, 2015, Molecular determinants of alpha-synuclein mutants’ oligomerization and membrane interactions, ACS chemical neuroscience, 6, 403, 10.1021/cn500332w · DOI
  6. Anderson, 2020, The non-amyloidal component region of α-synuclein is important for α-synuclein transport within axons, Frontiers in Cellular Neuroscience, 13, 540, 10.3389/fncel.2019.00540 · DOI
  7. Uversky, 2000, Why are “natively unfolded” proteins unstructured under physiologic conditions?, Proteins: Structure, Function, and Bioinformatics, 41, 415, 10.1002/1097-0134(20001115)41:3<415::AID-PROT130>3.0.CO;2-7 · DOI
  8. Farzadfard, 2022, The C-terminal tail of α-synuclein protects against aggregate replication but is critical for oligomerization, Communications Biology, 5, 123, 10.1038/s42003-022-03059-8 · DOI
  9. Fauvet, 2012, α-Synuclein in central nervous system and from erythrocytes, mammalian cells, and Escherichia coli exists predominantly as disordered monomer, Journal of Biological Chemistry, 287, 15345, 10.1074/jbc.M111.318949 · DOI
  10. Killinger, 2019, Endogenous alpha-synuclein monomers, oligomers and resulting pathology: let’s talk about the lipids in the room, NPJ Parkinson's Disease, 5, 23, 10.1038/s41531-019-0095-3 · DOI
  11. Kumari, 2021, Structural insights into α-synuclein monomer–fibril interactions, Proceedings of the National Academy of Sciences, 118, 10.1073/pnas.2012171118 · DOI
  12. Coskuner, 2013, Structures and free energy landscapes of the A53T mutant-type α-synuclein protein and impact of A53T mutation on the structures of the wild-type α-synuclein protein with dynamics, ACS Chemical Neuroscience, 4, 1101, 10.1021/cn400041j · DOI
  13. Wise-Scira, 2013, Structures and free energy landscapes of the wild-type and A30P mutant-type α-synuclein proteins with dynamics, ACS Chemical Neuroscience, 4, 486, 10.1021/cn300198q · DOI
  14. Bertoncini, 2005, Release of long-range tertiary interactions potentiates aggregation of natively unstructured α-synuclein, Proceedings of the National Academy of Sciences, 102, 1430, 10.1073/pnas.0407146102 · DOI
  15. Heise, 2005, Molecular-level secondary structure, polymorphism, and dynamics of full-length α-synuclein fibrils studied by solid-state NMR, Proceedings of the National Academy of Sciences, 102, 15871, 10.1073/pnas.0506109102 · DOI
  16. Dai, 2021, Optical tweezers-controlled hotspot for sensitive and reproducible surface-enhanced Raman spectroscopy characterization of native protein structures, Nature communications, 12, 1292, 10.1038/s41467-021-21543-3 · DOI
  17. Sandal, 2008, Conformational equilibria in monomeric α-synuclein at the single-molecule level, Plos Biol, 6, 10.1371/journal.pbio.0060006 · DOI
  18. Corti, 2019, Depicting conformational ensembles of α-synuclein by single molecule force spectroscopy and native mass spectroscopy, International Journal of Molecular Sciences, 20, 5181, 10.3390/ijms20205181 · DOI
  19. Vaikath, 2022, Structural and biophysical characterization of stable alpha-synuclein oligomers, International Journal of Molecular Sciences, 23, 14630, 10.3390/ijms232314630 · DOI
  20. Emin, 2022, Small soluble α-synuclein aggregates are the toxic species in Parkinson’s disease, Nature communications, 13, 5512, 10.1038/s41467-022-33252-6 · DOI
  21. Winner, 2011, In vivo demonstration that α-synuclein oligomers are toxic, Proceedings of the National Academy of Sciences, 108, 4194, 10.1073/pnas.1100976108 · DOI · Paper page
  22. Buell, 2014, Solution conditions determine the relative importance of nucleation and growth processes in α-synuclein aggregation, Proceedings of the National Academy of Sciences, 111, 7671, 10.1073/pnas.1315346111 · DOI
  23. Chamachi, 2017, Temperature-induced misfolding in prion protein: evidence of multiple partially disordered states stabilized by non-native hydrogen bonds, Biochemistry, 56, 833, 10.1021/acs.biochem.6b01042 · DOI
  24. Li, 2018, Amyloid fibril structure of alpha-synuclein determined by cryo-electron microscopy, Cell Research, 28, 897, 10.1038/s41422-018-0075-x · DOI
  25. Chakraborty, 2019, Cryo-electron microscopy uncovers key residues within the core of alpha-synuclein fibrils, ACS Chemical Neuroscience, 10, 1135, 10.1021/acschemneuro.9b00090 · DOI
  26. Zhao, 2020, Parkinson's disease associated mutation E46K of alpha-synuclein triggers the formation of a distinct fibril structure, Nat Commun, 11, 2643, 10.1038/s41467-020-16386-3 · DOI
  27. Sun, 2021, The hereditary mutation G51D unlocks a distinct fibril strain transmissible to wild-type alpha-synuclein, Nat Commun, 12, 6252, 10.1038/s41467-021-26433-2 · DOI
  28. Sun, 2023, Cryo-EM structure of amyloid fibril formed by alpha-synuclein hereditary A53E mutation reveals a distinct protofilament interface, The Journal of Biological Chemistry, 299, 10.1016/j.jbc.2023.104566 · DOI
  29. Fusco, 2017, Structural basis of membrane disruption and cellular toxicity by alpha-synuclein oligomers, Science, 358, 1440, 10.1126/science.aan6160 · DOI
  30. Galvagnion, 2019, Lipid dynamics and phase transition within alpha-synuclein amyloid fibrils, The Journal of Physical Chemistry Letters, 10, 7872, 10.1021/acs.jpclett.9b03005 · DOI
  31. Sarchione, 2021, Alpha-synuclein and lipids: the elephant in the room?, Cells, 10, 10.3390/cells10092452 · DOI
  32. Dasari, 2022, Untwisted alpha-synuclein filaments formed in the presence of lipid vesicles, Biochemistry, 61, 1766, 10.1021/acs.biochem.2c00283 · DOI
  33. Dou, 2022, Phosphatidylcholine and phosphatidylserine uniquely modify the secondary structure of alpha-synuclein oligomers formed in their presence at the early stages of protein aggregation, ACS Chemical Neuroscience, 13, 2380, 10.1021/acschemneuro.2c00355 · DOI
  34. Frieg, 2022, The 3D structure of lipidic fibrils of alpha-synuclein, Nat Commun, 13, 6810, 10.1038/s41467-022-34552-7 · DOI
  35. Yu, 2015, Transient beta-hairpin formation in alpha-synuclein monomer revealed by coarse-grained molecular dynamics simulation, The Journal of chemical physics, 143, 10.1063/1.4936910 · DOI
  36. Zhang, 2022, Molecular insights into the misfolding and dimerization dynamics of the full-length alpha-synuclein from atomistic discrete molecular dynamics simulations, ACS Chemical Neuroscience, 13, 3126, 10.1021/acschemneuro.2c00531 · DOI
  37. Zamel, 2023, Structural and dynamic insights into alpha-synuclein dimer conformations, Structure, 31, 411, 10.1016/j.str.2023.01.011 · DOI
  38. Ashish, 2007, Krueger, Global structure changes associated with Ca2+ activation of full-length human plasma gelsolin, The Journal of Biological Chemistry, 282, 25884, 10.1074/jbc.M702446200 · DOI
  39. Badmalia, 2018, Bonsai gelsolin survives heat induced denaturation by forming beta-amyloids which leach out functional monomer, Scientific Reports, 8, 12602, 10.1038/s41598-018-30951-3 · DOI
  40. Pandey, 2014, Low pH overrides the need of calcium ions for the shape-function relationship of calmodulin: resolving prevailing debates, The Journal of Physical Chemistry. B, 118, 5059, 10.1021/jp501641r · DOI
  41. Ashish, 2022, Visualizing how inclusion of higher reciprocal space in SWAXS data analysis improves shape restoration of biomolecules: case of lysozyme, Journal of Biomolecular Structure & Dynamics, 40, 12975, 10.1080/07391102.2021.1977704 · DOI
  42. Chauhan, 2023, Structural and functional characterization of Rv0792c from mycobacterium tuberculosis: identifying small molecule inhibitor against hutc protein, Microbiology Spectrum, 11, 10.1128/spectrum.01973-22 · DOI
  43. Sharma, 2020, Heat induces end to end repetitive association in P. furiosus L-asparaginase which enables its thermophilic property, Scientific Reports, 10, 21702, 10.1038/s41598-020-78877-z · DOI
  44. Singh, 2020, Biochemical characterization of ClpB protein from Mycobacterium tuberculosis and identification of its small-molecule inhibitors, International Journal of Biological Macromolecules, 165, 375, 10.1016/j.ijbiomac.2020.09.131 · DOI
  45. Tashiro, 2008, Characterization of fibrillation process of α-synuclein at the initial stage, Biochemical and Biophysical Research Communications, 369, 910, 10.1016/j.bbrc.2008.02.127 · DOI
  46. Rekas, 2010, The structure of dopamine induced alpha-synuclein oligomers, European Biophysics Journal, 39, 1407, 10.1007/s00249-010-0595-x · DOI
  47. Ullman, 2011, Explaining the structural plasticity of alpha-synuclein, Journal of the American Chemical Society, 133, 19536, 10.1021/ja208657z · DOI
  48. Curtain, 2015, Alpha-synuclein oligomers and fibrils originate in two distinct conformer pools: a small angle X-ray scattering and ensemble optimisation modelling study, Molecular bioSystems, 11, 190, 10.1039/C4MB00356J · DOI
  49. van Maarschalkerweerd, 2015, Formation of covalent di-tyrosine dimers in recombinant alpha-synuclein, Intrinsically Disordered Proteins, 3, 10.1080/21690707.2015.1071302 · DOI
  50. Herranz-Trillo, 2017, Structural analysis of multi-component amyloid systems by chemometric SAXS data decomposition, Structure, 25, 5, 10.1016/j.str.2016.10.013 · DOI
  51. Chung, 2019, Alpha-synuclein sterically stabilizes spherical nanoparticle-supported lipid bilayers, ACS Applied Biomaterials, 2, 1413, 10.1021/acsabm.8b00774 · DOI
  52. Moretti, 2020, Comprehensive Structural and thermodynamic analysis of prefibrillar WT alpha-synuclein and Its G51D, E46K, and A53T mutants by a combination of small-angle x-ray scattering and variational bayesian weighting, Journal of Chemical Information and Modeling, 60, 5265, 10.1021/acs.jcim.0c00807 · DOI
  53. Panuganti, 2022, Oligomers, fibrils and aggregates formed by alpha-synuclein: role of solution conditions, Journal of Biomolecular Structure & Dynamics, 40, 4389, 10.1080/07391102.2020.1856721 · DOI
  54. Lindsay, 2021, Effects of pH on an IDP conformational ensemble explored by molecular dynamics simulation, Biophysical Chemistry, 271, 10.1016/j.bpc.2021.106552 · DOI
  55. Ahmed, 2021, Refinement of alpha-synuclein ensembles against SAXS data: comparison of force fields and methods, Frontiers in Molecular Biosciences, 8, 10.3389/fmolb.2021.654333 · DOI
  56. van Raaij, 2006, Quantitative morphological analysis reveals ultrastructural diversity of amyloid fibrils from α-synuclein mutants, Biophysical Journal, 91, L96, 10.1529/biophysj.106.090449 · DOI
  57. Solanki, 2014, Global shape and ligand binding efficiency of the HIV-1-neutralizing antibodies differ from those of antibodies that cannot neutralize HIV-1, Journal of Biological Chemistry, 289, 34780, 10.1074/jbc.M114.563486 · DOI
  58. Ahmed, 2021, Refinement of α-synuclein ensembles against SAXS data: comparison of force fields and methods, Frontiers in Molecular Biosciences, 8, 10.3389/fmolb.2021.654333 · DOI
  59. Vasudeva, 2021, Shape-function of a novel metapyrocatechase, RW4-MPC: Metagenomics to SAXS data based insight into deciphering regulators of function, Int J Biol Macromol, 188, 1012, 10.1016/j.ijbiomac.2021.08.031 · DOI
  60. Kumar, 2021, Elicitation of potent serum neutralizing antibody responses in rabbits by immunization with an HIV-1 clade C trimeric Env derived from an Indian elite neutralizer, PLoS Pathogens, 17, 10.1371/journal.ppat.1008977 · DOI
  61. Goel, 2022, Plasmodium falciparum Kelch13 and its artemisinin-resistant mutants assemble as hexamers in solution: a SAXS data-driven modelling study, The FEBS Journal, 289, 4935, 10.1111/febs.16378 · DOI
  62. Manalastas-Cantos, 2021, ATSAS 3.0: expanded functionality and new tools for small-angle scattering data analysis, J Appl Crystallogr, 54, 343, 10.1107/S1600576720013412 · DOI
  63. Kozin, 2001, Automated matching of high- and low-resolution structural models, J Appl Crystallogr, 34, 33, 10.1107/S0021889800014126 · DOI
  64. Volkov, 2003, Uniqueness of ab initio shape determination in small-angle scattering, J Appl Crystallogr, 36, 860, 10.1107/S0021889803000268 · DOI
  65. Petoukhov, 2012, New developments in the ATSAS program package for small-angle scattering data analysis, J Appl Crystallogr, 45, 342, 10.1107/S0021889812007662 · DOI
  66. Tria, 2015, Advanced ensemble modelling of flexible macromolecules using X-ray solution scattering, Iucrj, 2, 207, 10.1107/S205225251500202X · DOI
  67. Badmalia, 2017, Visualizing temperature mediated activation of gelsolin and its deactivation by Pip(2): a Saxs based study, Scientific Reports, 7, 4670, 10.1038/s41598-017-04975-0 · DOI
  68. Svergun, 1995, CRYSOL - a program to evaluate x-ray solution scattering of biological macromolecules from atomic coordinates, J Appl Crystallogr, 28, 768, 10.1107/S0021889895007047 · DOI
  69. Guo, 2022, AlphaFold2 models indicate that protein sequence determines both structure and dynamics, Scientific Reports, 12, 10696, 10.1038/s41598-022-14382-9 · DOI · Paper page
  70. Krieger, 2002, Increasing the precision of comparative models with YASARA NOVA--a self-parameterizing force field, Proteins, 47, 393, 10.1002/prot.10104 · DOI
  71. Krieger, 2014, YASARA view - molecular graphics for all devices - from smartphones to workstations, Bioinformatics, 30, 2981, 10.1093/bioinformatics/btu426 · DOI
  72. Ozvoldik, 2023, YASARA model-interactive molecular modeling from two dimensions to virtual realities, Journal of Chemical Information and Modeling, 63, 6177, 10.1021/acs.jcim.3c01136 · DOI
  73. Panjkovich, 2016, Deciphering conformational transitions of proteins by small angle X-ray scattering and normal mode analysis, Phys Chem Chem Phys, 18, 5707, 10.1039/C5CP04540A · DOI · Paper page
  74. Paleologou, 2008, Phosphorylation at Ser-129 but not the phosphomimics S129E/D inhibits the fibrillation of α-synuclein, Journal of Biological Chemistry, 283, 16895, 10.1074/jbc.M800747200 · DOI
  75. Araki, 2016, A small-angle X-ray scattering study of alpha-synuclein from human red blood cells, Scientific reports, 6, 30473, 10.1038/srep30473 · DOI
  76. Ohgita, 2022, Mechanisms of enhanced aggregation and fibril formation of Parkinson’s disease-related variants of α-synuclein, Scientific Reports, 12, 6770, 10.1038/s41598-022-10789-6 · DOI
  77. Sode, 2007, Effect of reparation of repeat sequences in the human α-synuclein on fibrillation ability, International Journal of Biological Sciences, 3, 1, 10.7150/ijbs.3.1 · DOI
  78. Ghosh, 2015, Structure based aggregation studies reveal the presence of helix-rich intermediate during α-Synuclein aggregation, Scientific Reports, 5, 9228, 10.1038/srep09228 · DOI
  79. Louis-Jeune, 2012, Prediction of protein secondary structure from circular dichroism using theoretically derived spectra, Proteins: Structure, Function, and Bioinformatics, 80, 374, 10.1002/prot.23188 · DOI
  80. Rost, 1994, PHD-an automatic mail server for protein secondary structure prediction, Bioinformatics, 10, 53, 10.1093/bioinformatics/10.1.53 · DOI
  81. Ema, 2022, Protein secondary structure prediction by a neural network architecture with simple positioning algorithm techniques, International Journal of Electrical and Computer Engineering, 12, 4380, 10.11591/ijece.v12i4.pp4380-4390 · DOI
  82. King, 1997, DSC: public domain protein secondary structure predication, Computer Applications in the Biosciences, 13, 473
  83. Lin, 2005, A simple and fast secondary structure prediction method using hidden neural networks, Bioinformatics, 21, 152, 10.1093/bioinformatics/bth487 · DOI
  84. Patel, 2014, Knowledge base and neural network approach for protein secondary structure prediction, Journal of Theoretical Biology, 361, 182, 10.1016/j.jtbi.2014.08.005 · DOI
  85. King, 1996, Identification and application of the concepts important for accurate and reliable protein secondary structure prediction, Protein Science, 5, 2298, 10.1002/pro.5560051116 · DOI
  86. Levin, 1997, Exploring the limits of nearest neighbour secondary structure prediction, Protein Engineering, 10, 771, 10.1093/protein/10.7.771 · DOI
  87. Levin, 1986, An algorithm for secondary structure determination in proteins based on sequence similarity, FEBS Letters, 205, 303, 10.1016/0014-5793(86)80917-6 · DOI
  88. Tamposis, 2021, Hidden neural networks for transmembrane protein topology prediction, Computational and Structural, Biotechnology Journal, 19, 6090
  89. Koswatta, 2012, A simple comparison between specific protein secondary structure prediction tools, Tropical Agricultural Research, 23, 10.4038/tar.v23i1.4636 · DOI
  90. Dong, 2018, Structural characteristics and membrane interactions of tandem α-synuclein oligomers, Scientific Reports, 8, 6755, 10.1038/s41598-018-25133-0 · DOI
  91. Konijnenberg, 2016, Opposite structural effects of epigallocatechin-3-gallate and dopamine binding to α-synuclein, Analytical Chemistry, 88, 8468, 10.1021/acs.analchem.6b00731 · DOI
  92. Ziaunys, 2021, Polymorphism of alpha-synuclein amyloid fibrils depends on ionic strength and protein concentration, International Journal of Molecular Sciences, 22, 12382, 10.3390/ijms222212382 · DOI
  93. Fink, 2006, The aggregation and fibrillation of α-synuclein, Accounts of Chemical Research, 39, 628, 10.1021/ar050073t · DOI
  94. Munishkina, 2009, Accelerated fibrillation of α-synuclein induced by the combined action of macromolecular crowding and factors inducing partial folding, Current Alzheimer Research, 6, 252, 10.2174/156720509788486491 · DOI

Metadata

Authors
Dey, M., Gupta, A., Badmalia, M. D., Ashish, & Sharma, D.
Affiliations
Institute of Microbial Technology (IN)
Corresponding author
  • Ashish
  • Sharma, Deepak
ORCID
Venue
International Journal of Biological Macromolecules
Publisher
Elsevier BV
Published
2025-02
Volume
288
Pages
138614
ISSN
0141-8130 (ISSN-L)
DOI
10.1016/j.ijbiomac.2024.138614
OpenAlex
W4405316207
Cited by
5
References
4
Citations by year
2026: 1 · 2025: 3
FWCI
0.68
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Biological Macromolecules (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
  • CSIR - Institute of Microbial Technology (50/2023)
  • Council of Scientific & Industrial Research
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Health Sciences › Medicine › Clinical Neurology › Parkinson's Disease Mechanisms and Treatments
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
Keywords
alpha-synuclein, conformational ensembles, Gaussian chain model, twisted conformation, solution structure, molecular dynamics, normal mode analysis, protein disorder
MeSH terms
Humans, Models, Molecular, Protein Conformation, Solutions, X-Ray Diffraction, Normal Distribution, alpha-Synuclein, Scattering, Small Angle, Molecular Dynamics Simulation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review